- Focus on key performance indicators: being a great communication tool in themselves KPIs can also help condense vast amounts of data into initial insights. With the right tools you can then later drill into the underlying data
- Clarify what you want to achieve: is it about analysing pricing data, or is it about optimisation or simulation of future pricing? Especially with large amounts of data the objective is important to keep in mind.
- Start with manageable subsets of data to begin with, and prove value to the organisation.
- Get the right tools: with millions or even billions of price elements to analyse and simulate you need a strong IT tool. Excel can in many cases still be used for some of the front-end analysis, but the crunching is best left to a dedicated tool.
At Stratinis we have been working with Big Pricing Data for a long term. Some examples include:
- Global consumer goods manufacturer: analysis and simulation of price waterfalls across 40000 products, 60000 direct customers, 40 countries and 80 different discount types
- Manufacturer of medical devices: 300000 customers, 20000 products, 35 countries
- Electronics manufacturer: price planning across 90000 products, sold in 80 countries
- Online trading platform: 300000 prices quoted every week
- Software vendor: 2 million product configurations (PC, Mac, Linuc etc)